The present invention relates to the flat panel displays based on liquid crystal display (LCD) and other related display technologies such as organic light emitting devices (OLED), and more particularly to the inspection of components formed on such displays.
During the manufacturing of LC displays, large clear plates of thin glass are used as a substrate for the deposition of thin film transistor (TFT) arrays. Usually, several independent TFT arrays are contained within one glass substrate plate and are often referred to as TFT panels. Alternatively, an active matrix LCD, or AMCLD, covers the class of displays utilizing a transistor or diode at every subpixel, and therefore encompasses TFT devices, such glass substrate plates may also be referred to as AMLCD panels. Flat panel displays may also be fabricated using any of the OLED technologies and though typically fabricated on glass, may also be fabricated on plastic substrate plates.
TFT pattern deposition is performed in a multitude of stages where in each stage, a particular material (such as a metal, indium tin oxide (ITO), crystalline silicon, amorphous silicon, etc.) is deposited on top of a previous layer (or glass) in conformity with a predetermined pattern. Each stage typically includes a number of steps such as deposition, masking, etching, stripping, etc.
During each of these stages and at various steps within each stage, many production defects may occur that may affect the electrical and/or optical performance of the final LCD product. Such defects include but are not limited to metal protrusion 110 into ITO 112, ITO protrusion 114 into metal 116, a so-called mouse bite 118, an open circuit 120, a short 122 in a transistor 124, and a foreign particle 126, as shown in FIG. 1. Other defects include mask problems, over or under etching, etc.
Even though the TFT deposition processes are tightly controlled, defect occurrence is unavoidable. This limits the product yield and adversely effects production costs. Typically, the TFT arrays are inspected using one or multiple Automated Optical Inspection (AOI) system(s) following critical deposition stages and by an opto-electrical inspection machine, also referred to as array tester or array checker (AC) to test the finished TFT arrays. Commonly AOI and AC systems provide defect coordinates; they do not provide high resolution images required to classify defects as killer, reparable or just imperfections not affecting the TFT array performance (so called process defects). The defect coordinate information is passed to a TFT array repair tool, also referred to as array saver (AS), and such classification is conventionally done manually by the TFT array repair machine operator.
The average number of defects per plate may vary from one TFT array manufacturer to another and from one manufacturing plant to another. Typically, the defect review and repair capacity within the TFT array fabrication line is sized to process 300-400 defects per 7th generation plates. Typically 5 to 10% of defects per plate are assumed to require repair.
Since the TFT array features are typically very small (sub-pixel size may be 80×240 μm and up to 216×648 um for large 40 inch LCD televisions made from 7th generation plates), the array repair tool includes a microscope to perform a defect review to decide whether the defect is repairable. The microscope field of view is small (ranging from 100×100 μm to 2×2 mm) relative to the plate size (typically 2.1×2.4 m). The microscope is installed on a precision XY stage so that it could be dispatched from one defect to another. The defect coordinates are known from inspections carried out earlier by AOI and AC inspection systems. The glass plate remains immobilized under the XY stage by means of a vacuum chuck during the defect review and repair. Following the review, the repairable defects are typically treated by means of laser trimming, laser welding or by bridging open line defects typically using a chemical vapor deposition (CVD) technique.
The above series of general events is typical of all array repair tools. However, because the number, type, locations, size/degree of defects often vary from panel to panel, a means to pass judgment is required at almost all of the tool steps following capture of the defect images—for example, whether an image is truly a defect rather than nuisance, what kind of defect has been found, whether or not a specific defect needs repair, what kind of repair is required, what repair parameters are needed, what is the next defect to be repaired, and so forth. Many repair tools combine tool operation with human operator judgment and intervention to identify, classify, and then repair such defects.
FIGS. 2 and 3 show two defect repair examples in cross sections. Metal protrusion defect 110 is shown in FIG. 2A (see FIG. 1 for top view). In this example, after identifying and classifying the defect 110, a repair recipe is created, and then executed to remove the protrusion, as shown in FIG. 2B. FIGS. 3A-3E represent the repair steps performed to correct an open path between metal lines 32 and 34 (See defect in 120 FIG. 1). In this example, a laser 36 is used to break through (“zap”) the passivation layer 38 and cut into the metal lines. Then, a means to deposit material (in this case, a chemical vapor gas and moving laser energy source) is introduced to create contact electrodes 42 and 44 on the metal lines 32 an 34. Thereafter a metal line 46 is formed to connect the two metal lines 32 and 34. In both examples, the repair function of the tool must accurately locate the defect or portions of the defect area to be repaired, and the repair process must be specified for the particular defect type. Since not all panel fabrication processes are identical, parameters related to power, spot size, gas flow or other material deposition parameters, and so forth may need to be adjusted on a per panel or per panel product basis.
As shown in FIG. 4, once the defect image is taken 402, an operator reviews the image 403, categorizes the potential defects into nuisance and defect categories such as open, short, and others as illustrated in FIG. 1, and decides whether or not a repair is necessary 404. If no repair is necessary, the operator instructs the tool to move to the next defect 405 and the process for image capture and defect classification is repeated. If the operator decides that a repair is needed, he/she then determines the kind of repair needed (for example, a cut or a perhaps a connection), and sets the basic repair functions (such as laser cut) parameters including determining the start and end points for the cut or connection 406. The operator then instructs the machine to execute the repair 408. The tool then repairs the defect 410, and then advances to the next defect 412 and the process is repeated.
The operator may determine that the needed repair parameters and sequence instructions (or recipe) are the same as a previous recipe, and may choose to use stored recipes rather than creating a new one. Sometimes a single plate has numerous and different kinds of defects, and the operator prioritizes the order in which the repairs are made. For example, the operator may choose to first repair defects of a certain first type, followed by defects of a second type, and so forth. Or, the operator may choose to repair defects in a spatial sequence (for example, left to right, front end to back end of the panel).
Human judgment currently may be applied at most every step of the review/detection and repair of flat panels. Panel fabricators often seek both minimizing cost and optimizing time, and a repair tool operating automatically may be highly sought. However, as implied above, an automated tool must provide equal or better consistent judgment results at rates at least as quick as humans. The review and repair of panel defects offers several challenges that must be considered in the development of an automatic tool. First, detection, which typically relies on optical means, will produce images whose quality may vary within panels or over a series of panels in contrast, brightness, color, and other similar parameters. Such variations are brought into consideration by an operator during assessment of a potential defect image, and thus a means for automatic assessment will also need to be devised. The operator typically recognizes and identifies the defect type at approximately the same instant as determining whether or not a potential image is indeed a defect. However, due to variations in image quality, the operator may mistakenly identify the defect type or bin inconsistently or with ambiguity. Thus, an automated tool must address the challenge of binning defects accurately and consistently. A further challenge arises at the time of factory start-up, when the available classification data may be scarce or not accurate. An automated tool should therefore provide a means to build its own libraries of classification rules based on the accumulation of training examples and/or statistical data collected by the tool over time. Finally, once in full production, LCD panel fabricators will prefer repair tools that do not require full time attention by an operator.
In an article entitled “Automatic Repair in AMLCD” April 1994, Proc. SPIE Vol. 2174, p. 98-106, Advanced Flat Panel Display Technologies, Peter S. Friedman, Qiu et al recognized the need for automatic repair, and analyzed ways to enable automation. However, LCD panel technologies were relatively simple at the time and the decision-making rules within the architecture proposed by Qiu et al were based on a relatively small set of options. For example, only two defect types were recognized: “Open” or “Short”, with four subclasses of “open” and three subclasses of “short.” Further, three “open” repair functions were defined, while only a single “short” repair function was considered.
Since the publication of the above article, LCD panel fabrication has grown in complexity with the increase in use case for LCD panels ranging from computer laptop screens to computer monitors to television screens. For example, more material types and material combinations have been introduced; pixel designs now include a variety of sizes and shapes. In addition, panel fabricators wish to make repairs on a variety of layers. A need continues to exist for automating the array repair tool process steps to increase accuracy and to allow an operator to oversee more than one tool.